In the explainable AI space, several research projects [1]}, [2]}, [3]} have begun to explore how to support end-user specific needs such as ours.
Wang et al. [2]} present a framework for aligning explainable AI practices with human reasoning approaches, and they test this framework in a co-design exercise using a prototype CDSS. However, their framework isn't in a machine-readable format and hence is difficult to reuse for supporting explanations in a system. The findings from their co-design process that clinicians seek different explanations in different scenarios corroborate with those from our cognitive walkthrough, and EO can help support system designers in this endeavor. Similarly, Dragoni et al. [3]} propose a rule-based explanation system in the behavior change space capable of providing trace based explanations to end-users to encourage better lifestyle and nutrition habits. While this explanation system was tested with real target users and subject matter experts, it is limited in scope by the types of explanations it provides. Vera et al. [1]} have released a question bank of explanation question types that can be used to drive implementations such as ours.
